Innovative Lightweight CNN Model for Stage-Wise Alzheimer's Disease Detection

A common neurological condition that causes cognitive decline, particularly in older people, is Alzheimer's disease (AD). In order to improve patient treatment and minimize the progression of AD, early and precise recognition is essential. Complex and expert-dependent procedures are frequently used to diagnose the clinical phases of AD. Early-stage detection might be greatly advanced by the rapid growth of neuroscience research of brain imaging. In the suggested model, a feature-rich architecture is used that is tailored to improve learning representation in various stages of Alzheimer's disease. Faster processing is made possible by its lightweight architecture, which also makes it feasible to incorporate into real-time healthcare systems. In order to classify the various stages of Alzheimer's disease, this study combines deep learning with brain imaging from magnetic resonance imaging. MobileNet, DenseNet121, InceptionV3, VGG19, Xception, and CustomCNN were among the many models that underwent testing. The data set used to test the model includes 83,860 brain MRI images generated from Kaggle. At 99.10% classification accuracy, the results of the experiments show that CustomCNN performs noticeably better than the other models. With an error rate of 0.009, evaluations using metrics such as confusion matrices, precision, recall, F1 score, error rate, AUC, and average mean further validate the effectiveness of the suggested model.